Many enterprise GPUs run a single model during inference — even when it uses only ~30% of memory. So how much capacity is being left on the table? In our latest benchmark with @nebiusai, we used NVIDIA Run:ai fractional GPU allocation and NVIDIA NIM to measure real-world impact on throughput, latency, and concurrency. What we found: ✅ 86% of full GPU capacity using just a 0.5 slice ✅ 3× more users with mixed workloads on shared GPUs ✅ Near-linear scaling down to 0.125 slices ✅ Zero latency cliffs during autoscaling Stop GPU fragmentation. Start maximizing throughput. 🔗 Read the full deep dive: nvda.ws/40gIlsj